ASTRA — Unified Research Lab + MCP Server

Server Quality Checklist

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Latest release: v2.6.0
Disambiguation4/5
Tool names are descriptive and use domain prefixes (np_, sensor_, tcai_, wm_) which help differentiate them. However, the high number of tools (50) and some overlapping status functions (e.g., get_snn_state, get_system_status) may cause occasional confusion, though descriptions are detailed.
Naming Consistency5/5
Naming is highly consistent: each domain uses a clear prefix (tcai_, np_, sensor_, wm_), followed by descriptive verb_noun snake_case. Exceptions are few (e.g., get_acm_score) but still follow the same pattern. No mixing of casing or styles.
Tool Count3/5
With 50 tools, the server is on the heavy side, but the scope (Unified Research Lab with multiple subsystems) justifies the count. The prefix grouping mitigates navigation issues, yet the sheer number may still overwhelm an agent.
Completeness3/5
The tool surface covers core operations for simulations, consciousness, sensors, and world model, but lacks certain lifecycle management tools (e.g., no list/delete for memories, no sensor configuration). Dead ends exist, such as no update tool for system parameters beyond set_parameter.
Average 2.8/5 across 50 of 50 tools scored. Lowest: 1.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  "maintainers": [
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. The minimal description does not indicate whether the tool is destructive, requires permissions, has side effects, or affects the system state. It fails to provide any behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While extremely short, the description sacrifices necessary detail. It is under-specified rather than concise; every sentence should earn its place, but here there is only one sentence that adds no value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has two parameters, no output schema, and no annotations, the description is completely inadequate. It provides no context about return values, side effects, or when to use the tool, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description offers no explanation of the parameters (neuronIds, strength). It adds no meaning beyond the schema itself, leaving the agent to guess their purpose and constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Spike Injection' is essentially a tautology of the tool name, providing no specific verb or resource. It does not distinguish from sibling tools like np_count_spikes or np_query_spike_events, which also deal with spikes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. There is no mention of prerequisites, context, or what differentiates it from other spike-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but fails to disclose any behavioral traits such as what information is returned, whether it is read-only, or any side effects. The description is wholly insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief but does not earn its place due to lack of informative content. This is under-specification, not conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description must provide complete context about what the tool returns or does. It fails entirely, offering no useful information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although there are zero parameters (baseline 4), the description fails to add meaning beyond the empty schema. It does not explain what 'Bio-Computing Platforms' means in terms of output or purpose, so the score is reduced.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Bio-Computing Platforms' is extremely vague and does not state a specific verb or resource action. It barely restates the tool name and provides no clarity on what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_system_status, get_acm_score, or various status tools. The description offers no context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description offers no behavioral details (e.g., whether the tool is read-only, destructive, or has side effects). The agent cannot infer safety or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 3-word phrase, which is too brief to be useful. It fails to front-load any actionable information and is essentially under-specification, not concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of cross-modal fusion and lack of output schema, the description is severely incomplete. It does not explain what the tool returns or how parameters affect behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not explain the three boolean parameters (includeVisual, includeAudio, includeOlfactory). The agent must guess their meaning from names alone, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Cross-Modal Attention Fusion' hints at combining multiple sensory modalities, but lacks a clear verb specifying what the tool does (e.g., fuses, merges, or processes). It does not differentiate from sibling tools like sensor_process or individual sensor tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as sensor_visual, sensor_audio, or sensor_olfactory. Missing context about prerequisites or use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the full burden. It only says 'Modify', implying mutation, but gives no details on side effects, permissions, or reversibility. This is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (3 words), which is concise but sacrifices necessary detail. It is under-specification, not efficient conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, two parameters, and a complex sibling set, the description is completely inadequate. It fails to convey essential behavior or usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain parameters. It adds no meaning to 'path' or 'value' beyond the schema, leaving the agent without guidance on their semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Modify State Parameter' is a tautology that restates the tool name 'set_parameter' without adding specificity. It fails to distinguish this tool from siblings like 'get_metrics' or 'snn_reset'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools, the absence of usage context leaves the agent uncertain.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description provides no behavioral traits. It does not disclose effects like whether start resets simulation state or if stop is reversible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Only two words, but drastically under-specified. Not concise—it is lacks necessary information, so it does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool with one parameter, no annotations, and no output schema, the description should at least state the commands' effects. It is completely inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the single parameter 'command'. The enum values are not explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Simulation Control' is a vague noun phrase that does not specify the action. It could mean controlling simulation parameters or executing commands; the purpose is unclear without inferring from the parameter enum.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like get_platform_status or snn_step. No context on which command (start, stop, status) to choose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and only a four-word description, the tool gives zero insight into its behavioral traits. There is no disclosure of whether it is read-only, destructive, requires permissions, or has side effects. The agent cannot infer safety or expected impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (4 words) but not effectively concise. It lacks structure and fails to convey necessary information. A concise description would be informative yet brief; this one is under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the input schema (nested object), lack of annotations, and absence of output schema, the description is completely inadequate. The tool has no explanation of what the detection produces (e.g., a metric, a signal, an event) and no context about its role in the working memory ecosystem.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
While the schema has 100% coverage with descriptions for each property, the tool description adds no additional meaning. The description does not explain how the action parameters (targetNeurons, strengths, duration) relate to violation-of-expectation detection. The semantic connection between parameters and tool purpose is missing, leaving the agent without critical context to interpret parameter values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Violation-of-Expectation Detection' states a concept but lacks a specific verb and resource. It does not clarify whether this tool computes a surprise score, triggers an event, or returns data. It fails to distinguish from sibling tools like wm_predict or wm_plan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. There is no indication of when to use this tool versus alternatives, no prerequisites, and no mention of context where it should be avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no description of behavior, the agent is left entirely in the dark about whether this tool is read-only, has side effects, or requires permissions. The description fails to disclose any behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (only one phrase), which is concise but severely under-specified. It does not earn its place as a helpful description; it is merely a label.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and the presence of many sibling status tools, the description should explain what system status entails, what the return format is, and how it differs. It does none of this, leaving the tool contextually incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and the schema description coverage is 100%, so the baseline is 3. The description adds no value beyond the schema, but does not make things worse.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'ASTRA System Status' is essentially a tautology of the tool name 'get_system_status'. It does not specify what the tool does actively, only restates the name without adding a verb or distinct resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus sibling tools like 'get_platform_status', 'np_status', or 'sensor_status', which all likely provide status information. The agent receives no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations and no description of behavioral traits. The agent cannot infer if this is a read-only operation, has side effects, or requires authentication. The description provides zero behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short but under-specified. While concise, it sacrifices clarity and fails to earn its place by providing useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters or output schema, the description is the sole source of context. It is insufficient for an AI agent to understand the tool's purpose, inputs, outputs, or effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is trivially 100%. Baseline is 3. Description adds no parameter meaning, but schema already covers the empty set.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description is vague: 'Consciousness Assessment (Proxy)' does not clearly state what the tool does, what ACM stands for, or what score is returned. It is not a tautology but lacks specificity to distinguish from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description gives no context for invocation conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. However, it only states 'Online World Model Training Step' without mentioning any side effects, state changes, safety concerns, or what the action injection actually does to the model. Critical information about destructiveness or mutation is omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief (one phrase) but fails to convey essential information. It does not earn its place because it does not provide utility to the agent; it is under-specified rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameter structure and the tool's role in a world model training sequence, the description is completely inadequate. It lacks any explanation of training context, return behavior, or integration with other tools. The absence of an output schema further compounds the incompleteness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'action' is fully described in the schema with property descriptions (targetNeurons, strengths, duration). Since schema coverage is 100%, the description adds no extra meaning but does not detract. The baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a short phrase 'Online World Model Training Step' which vaguely indicates a training operation but doesn't specify what the tool actually accomplishes. It fails to distinguish itself from sibling tools like wm_encode or wm_plan, and doesn't explain the role of the action parameter in training.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool, what prerequisites are needed, or how it relates to other world model tools. The agent receives no context about whether to call this before or after other steps, or what conditions warrant training.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It provides no information about side effects (e.g., whether it is read-only or destructive), permissions needed, or system impact. The three-word description fails to convey any behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly terse; three words are insufficient for a tool with no other documentation. Important details are omitted, making it under-specified rather than efficiently concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is severely incomplete. It does not clarify the snapshot's scope, format, or any potential constraints, leaving the agent with insufficient information to understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so schema description coverage is 100% by default. However, the description does not add any semantic value beyond the schema and does not explain what the snapshot contains or its format, which would be helpful for a zero-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Full State Snapshot' indicates the tool exports a complete snapshot of the system state, but it is vague and does not specify what 'full state' encompasses. It does not distinguish itself from sibling tools like get_system_status or get_platform_status that also retrieve state information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, recommended contexts, or cases where this tool should be avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavior. It fails to mention that this is a read-only operation, what the state contains, or any side effects. The description is completely inadequate for transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Although extremely concise (3 words), the description is under-specified and fails to provide necessary information. It sacrifices completeness for brevity, making it inefficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, no annotations, and a 3-word description. For a state retrieval tool, the description should explain what is returned (e.g., 'current SNN engine state including spike rates, connectivity, etc.'). It is incomplete and insufficient for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters and the schema coverage is 100% (empty). Per rubric, baseline is 4. The description adds no value beyond the schema, but cannot be penalized for missing parameter details when none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'SNN Engine State' is vague and nearly a tautology of the tool name 'get_snn_state'. It does not specify a verb or differentiate from sibling status tools like 'get_system_status' or 'get_platform_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. With many sibling tools providing similar status information (e.g., simulation_control, get_metrics), the description offers no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It merely states 'Reset SNN Engine' without disclosing what reset entails (e.g., state, parameters, simulation), or whether it is destructive or reversible.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise (2 words) but at the expense of necessary detail. The description is under-specified for a reset operation, lacking behavioral context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain the tool's side effects and return value. It fails to do so, leaving the agent without critical context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameters, and schema coverage is 100%. The description does not add parameter-level detail, but none is required. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb 'Reset' and resource 'SNN Engine', making the basic purpose clear. However, it does not differentiate from sibling tools like tcai_reset or clarify scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., tcai_reset, simulation_control). The description provides no context for appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. 'Real-time Metrics' suggests continuous updating but doesn't clarify if it's polling, streaming, or the impact of calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise at two words, but it's too sparse, sacrificing clarity for brevity. Could be improved with a short, informative phrase.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and zero parameters, the description should explain return values or behavior. It fails, leaving the agent uninformed about what metrics are provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, and schema coverage is 100% trivially. Baseline is 3; description adds no parameter info but none is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Real-time Metrics' is vague; it doesn't specify what action is performed or what metrics are retrieved, barely adding beyond the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like tcai_metrics or other monitoring tools; the description lacks any usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose side effects, read-only nature, performance implications, or what constitutes a 'window'. Only a vague indication of return type.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Overly concise single sentence—lacks necessary detail such as parameter descriptions or return format. Efficiency is undermined by missing critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has 3 parameters, no output schema, and no annotations. Description omits return value structure, constraints, and differentiation from many siblings, making it inadequate for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description adds no explanation for parameters (window_sec, fsname, limit) beyond schema defaults. 'fsname' remains cryptic, and schema coverage is low (33%), so description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear statement: 'individual spike timings over a window' specifies the tool returns detailed timing data, distinguishing it from count-based siblings like np_query_spike_count.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this vs alternatives (e.g., np_query_spike_count for counts, np_query_triggers for triggers). Missing context on prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose whether the tool is read-only, modifies state, or has side effects. The agent gains no behavioral insight beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (3 words) but fails to provide necessary information for an agent to use the tool correctly. Under-specification harms usability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and only a cryptic description, the agent lacks essential context about what the tool returns and how to interpret results. Incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has zero parameters, so schema description coverage is 100% (trivial). Baseline is 3 per rules; description adds no additional parameter meaning, which is acceptable given no params exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'IRB Neural Welfare Check' adds some specificity beyond the name ('check_ethics'), but remains vague about what exactly is checked and what the output is. It does not clearly differentiate from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives; no context about prerequisites or typical scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits, but it does not. It fails to mention if this tool is destructive, modifies state, requires a running simulation, or any side effects. The agent must infer behavior from the tool name alone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, but this is under-specification rather than efficient communication. It lacks any structure or sentences. While not verbose, it fails to convey necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), a minimal description might suffice, but 'Advance SNN Simulation' is insufficient. It does not explain the effect, return value, or relationship to the simulation state. The agent cannot use this tool correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'steps' has schema constraints (min/max/default) but no description. The tool description does not explain what 'steps' represents (e.g., number of simulation steps, iteration count). Schema description coverage is 0%, and the description adds no value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advance SNN Simulation' provides a verb and a resource, giving a general sense of the tool's purpose. However, it lacks specificity about what 'advance' means (e.g., step forward in time, trigger an update). It is distinguishable from siblings like 'snn_reset' but not clearly differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines provided. The description does not indicate when to use this tool versus alternatives like 'simulation_control' or 'snn_reset'. There is no guidance on context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as read-only nature, output format, or any side effects. The description is too vague to inform the agent about the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single phrase. While it could be more informative, it wastes no words. The structure is minimal but acceptable for a zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description should provide more context. It merely states the subject without specifying what 'status' entails (e.g., metrics, health, connectivity). This is insufficient for an agent to understand the tool's functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema coverage is 100%. The baseline score of 4 is appropriate as the description does not need to add parameter information, and none is missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Platform & Controller Status' essentially restates the tool name 'np_status', adding only minor context. It lacks a specific verb (e.g., 'retrieve', 'get') and does not differentiate from sibling tools like 'get_platform_status' or 'get_system_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Given many sibling status tools, the agent has no basis to select 'np_status' over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only states a noun phrase without disclosing side effects, rate limits, or behavioral traits. For a read-like tool, this is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise at 4 words, but lacks structure or front-loading of key information. Could be expanded without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema or behavioral details. For a status tool, agents need to know what the response contains to use it effectively. Incomplete for practical decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0 parameters, so description adds nothing beyond schema. Baseline 3 as no parameter information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Multimodal Sensor Pipeline Status' names the resource but lacks a verb indicating the action (e.g., get, retrieve). It distinguishes from siblings like sensor_audio by specifying 'pipeline', but remains vague on what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use sensor_status vs siblings like get_system_status or np_status. Missing context about scope or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does not mention whether the tool is read-only or modifies state, nor does it explain side effects or requirements. The term 'CEM' is unexplained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and front-loaded, but it is too brief to convey necessary information. It is not verbose, but it sacrifices clarity for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain what the tool returns or how the output should be interpreted. Given the lack of an output schema and the complexity of planning, this omission leaves a significant gap for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for all parameters (100% coverage), and the description adds no additional meaning or context about how parameters are used or constrained beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'CEM Planning for Optimal Spike Injection' indicates the tool is for planning spike injection using CEM, but it does not explicitly state the action (e.g., generate vs. execute) or clearly distinguish it from related tools like inject_spikes or wm_predict.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as wm_predict or wm_train_step. The description lacks context on prerequisites, typical use cases, or when planning is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits (side effects, output format, dependencies). It only states a mapping transformation, omitting details about what the tool actually does (e.g., simulation, hardware encoding).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely terse—a single phrase—which sacrifices helpfulness for brevity. While front-loaded, it lacks sentences and fails to convey essential context. Every word should earn its place; here too few words are used to be informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameters and absence of output schema, the description is incomplete. It does not explain what the tool returns, how the encoding works, or the role of each parameter in the process. A more comprehensive description is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% per context, so baseline is 3. The description adds no parameter information beyond what the schema provides (defaults, types). It does not explain the meaning or typical use of parameters like numReceptors, temporalPhase, etc.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as olfactory encoding from chemoreceptor to latent space, which distinguishes it from other sensor tools (audio, visual). However, it lacks an explicit action verb (e.g., 'simulates', 'computes'), making it slightly less direct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, nor any prerequisites or conditions. The description provides no context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether it is read-only, whether it requires initialization, or what side effects occur. Minimal information beyond the name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (4 words), but this brevity sacrifices necessary detail. It is not a tautology, but it fails to front-load distinctive information that would help the agent select this tool over many similar siblings.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and the presence of many sibling status/metrics tools, the description is insufficiently complete. It does not explain what the returned status/metrics contain, nor does it clarify the scope (e.g., read-only, real-time, historical).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is 100%. The description adds no parameter-specific information, but none is needed. Baseline score for no parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'World Model Status & Metrics' indicates the tool provides status/metrics for the world model, but it lacks specificity about what exactly is included and does not differentiate from sibling tools like 'get_metrics', 'get_system_status', or 'tcai_metrics'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as 'get_system_status' or 'np_status'. There is no mention of prerequisites or context, leaving the agent without direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It mentions 'closed-loop' but does not explain implications (e.g., is it safe? destructive?). The description fails to clarify whether this is a read-only operation or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it includes unnecessary internal details like 'NeuroPlatform v2 — Closed-loop _count_spike'. Could be more front-loaded and focused on the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of sibling tools and no output schema, the description is too sparse. It does not explain the return format, how results are presented, or how this relates to other spike-counting tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds minimal value beyond the schema (e.g., 'most active electrodes' is already in top_k description). No additional semantic context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool counts spikes per electrode over a time window, which is a specific verb+resource. However, it does not differentiate from similar sibling tools like np_query_spike_count or np_query_spike_events, missing an opportunity to clarify uniqueness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as np_query_spike_count. There is no mention of prerequisites, context, or exclusions, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It only mentions the encoding pipeline and does not address side effects, resource requirements (e.g., microphone access), read-only nature, or output details. This is insufficient for safe and effective tool use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one line). While it avoids verbosity, it sacrifices necessary detail for a tool with nested parameters and a complex pipeline. The structure could be improved by adding brief parameter context and expected output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested object with 6 sub-properties, no output schema, no annotations), the description is inadequate. It omits critical information such as return type, parameter behavior, and usage prerequisites, leaving the agent with significant ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes all parameters (100% coverage) with descriptions for 'simulate' and 'frequency'. The tool description adds no additional meaning beyond the pipeline overview, so it neither enhances nor detracts from the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'A-JEPA Audio Encoding (Waveform → Mel → Latent)' clearly states the tool's function: encoding audio into a latent representation via mel spectrogram. It effectively distinguishes this tool from sibling sensors (e.g., sensor_visual, sensor_olfactory) by specifying the audio modality and processing pipeline. However, it could be more explicit about the verb (e.g., 'encode') and the output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives (e.g., sensor_olfactory, sensor_visual). It lacks contextual cues such as prerequisites, constraints, or examples, leaving the agent without direction on appropriate invocation scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral traits. It only states the pipeline's scope and output, but does not disclose side effects, required permissions, or execution behavior, leaving the agent uninformed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it sacrifices necessary detail. 'Fused z' is unexplained, and the structure front-loads the name-like content without expanding on purpose or behavior. It is not appropriately complete for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the three optional parameters and no output schema, the description should explain what 'z' represents, the fusion process, and the role of parameters. It fails to provide sufficient context for an agent to understand the tool's full effect and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions; it only repeats the concept of multimodal fusion. The agent gains no extra insight into parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description indicates it is a full multimodal pipeline fusing all modalities into 'z', which distinguishes it from per-modality sibling tools. However, 'Fused z' is ambiguous and not explicitly defined, slightly reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this pipeline versus individual sensor tools (e.g., sensor_audio) or the sensor_fuse tool. The lack of exclusion criteria or context for selection leaves the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. It states 'Visual Encoding' but does not describe side effects, real vs. simulated data, hardware requirements, or output format. The schema hints at simulation via 'simulate' parameter, but this is not mentioned in the description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one phrase), which is concise but lacks structure. It is not formatted for easy parsing by an AI agent (no sentences, no separation of concerns). Could be improved with a brief sentence or bullet points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 parameters (one nested object) and no output schema, the description is too minimal. It does not explain what the output is, how it integrates with other sensor tools, or when to use it. Sibling tools like sensor_audio and sensor_fuse are similarly named but this description offers no distinguishing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all parameters have descriptions in the schema). The description adds no additional meaning beyond the schema; it merely names the tool. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'V-JEPA 2 Visual Encoding (Image/Video)' clearly indicates the tool is for visual encoding using a specific model, handling images or videos. However, it does not explicitly distinguish from sibling tools like np_camera_capture or sensor_fuse, which may also process visual data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. Among sibling tools (e.g., np_camera_capture, sensor_audio, sensor_fuse), there is no differentiation or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist. The description lists four aspects of self-representation but does not disclose whether the tool has side effects, is read-only, requires specific preconditions, or returns transient vs persistent data. The behavioral impact is unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at one line, but it is a phrase rather than a complete sentence. It front-loads key terms. Slightly more structure (e.g., 'Returns the current self-representation state...') would improve it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not explain the return format, data structure, or how to interpret the state components. For a tool likely returning complex internal state, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100%. The description adds context about what the state includes (interoception, epistemic model, etc.), which provides meaning beyond trivial schema. Baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists aspects of self-representation state but omits the action verb (e.g., 'get', 'retrieve'). The purpose is implied as reading internal state, but it's vague compared to sibling tools that have clearer action verbs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like tcai_workspace_state or tcai_metaconsciousness. Sibling tools with similar purposes exist, but no differentiation is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure but only states it captures data and returns descriptor/viability. No mention of side effects, permissions, or safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at one line. Minor waste with 'NeuroPlatform v2' prefix, but overall efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description minimally conveys purpose and outputs. However, additional context about outputs or typical use would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides full coverage for the single parameter (mea) with its own description. The tool description adds no additional meaning, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('capture') and resource ('MEA camera'), and mentions outputs ('descriptor + viability'). No sibling tool performs a similar function, so differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives or any prerequisites. The description lacks context for usage decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It only states the basic action but does not indicate that it is a read-only query, the format of returned data, or any side effects. This is insufficient for the agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at 10 words, but it lacks critical information. It is not overly verbose, but the brevity sacrifices completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema, the description should explain what the tool returns. It does not. Additionally, it does not mention the organoid (implied by name) or any important behavioral details. The description is insufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for both parameters (window_sec and dedup) with 100% coverage. The tool description adds no additional semantic information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries triggers sent to an organoid over a window, distinguishing it from siblings like np_send_trigger (sends triggers) and np_query_spike_events (spikes). However, it does not explicitly mention the action 'query' which is implied by the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description solely describes what it does without mentioning conditions or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Lacks annotations, so description must fully disclose behavior. It mentions 'with inertia' hinting at temporal dynamics, but does not explain what inertia implies, side effects, or requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise, but it omits critical details; front-loads purpose but not parameter or behavioral info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no param descriptions, the description is insufficient for an agent to use the tool correctly; missing parameter semantics and behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and description offers no explanation of the 4 parameters; agent cannot infer meaning of rewardSignal, novelty, threat, controllability from 'raw signals'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool appraises raw signals into PAD emotional space, a specific verb+resource pair, and distinguishes from siblings like tcai_curiosity by mentioning 'with inertia' and the PAD model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use vs. alternatives; purpose is implied but no conditions or exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavior. It does not state that the tool is read-only, whether it has side effects, or any other traits. The listed state components are the only behavioral hint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that lists the key elements of the workspace state. It is front-loaded with the purpose, though it could benefit from a bit more structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should more comprehensively explain the return format and structure. It only names components, lacking details on data types, units, or how to interpret the state.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides full coverage. The description adds meaning by enumerating what the state comprises, which is helpful for understanding the return value, though it remains vague.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool provides the state of a Global Neuronal Workspace, listing its components (ignition, focus, qualia, etc.). However, it lacks an explicit verb (e.g., 'get' or 'retrieve') and does not differentiate from sibling tools like tcai_active_inference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not specify when to use this tool versus alternatives, nor any prerequisites or conditions. With many sibling tools, this omission leaves the agent without direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states it encodes, without disclosing side effects, safety, or what the encoding entails. Minimal behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It is appropriately short for a tool with no parameters, though it could include more context without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and a simple purpose, the description is adequate but lacks information on return values or when the operation is valid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema description coverage is 100% (trivially). The description adds no additional meaning beyond the schema, but baseline 3 applies per rules.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool encodes SNN state to latent space, using a specific verb and resource. It is distinct from sibling tools, as no other tool has 'encode' in its name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like wm_plan or wm_predict. The description provides no context for intended usage or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions second-order self-monitoring and composite-proxy level, but does not disclose whether the tool is read-only, modifies state, or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, but first sentence is dense with technical terms. Could be more concise or better front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers the main output components (stage, stability, meta-representation) but lacks details on return format, data structure, or how to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters in input schema (100% schema coverage), so baseline is 4. Description adds no parameter info, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it tracks longitudinal development and provides coarse stage, stability, and meta-representation score. It gives a verb ('tracking') and resource ('developmental stages'), though the jargon makes it slightly less accessible.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like tcai_metaconsciousness, tcai_second_order, or tcai_self_model. No context on prerequisites or typical scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior, but it only states the outcome (predict next state) without clarifying side effects, read-only nature, or what happens to the system state during prediction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but lacks structure. It could include more details in a front-loaded manner without adding length, but it is minimally adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (nested object, no output schema) and the lack of behavior details, the description is insufficient. It does not explain return values, error conditions, or how the prediction output can be used.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents both parameters. The description adds no extra semantics beyond the tool's purpose, maintaining the baseline score without enhancement.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The tool name 'wm_predict' combined with the description 'Predict Next SNN State in Latent Space' clearly specifies the action (predict) and the resource (next SNN state) with a specific domain (latent space). This distinguishes it from sibling tools like 'wm_encode' or 'wm_plan'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives, such as 'wm_surprise' or 'wm_train_step'. There is no mention of prerequisites or context for prediction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It describes the output (weighted score) and its proxy nature, but does not mention side effects, state dependencies, cost, or limitations. Agents are left uninformed about potential impacts or required state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief (two sentences) with no superfluous words. However, the use of jargon like 'MetaconsciousnessEvaluator port' and 'meta-representation capacity' may reduce clarity for less specialized agents.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's conceptual complexity, the description is somewhat incomplete. It does not explain the output format (e.g., numeric range), how to interpret the score, or what state (if any) it queries. For a simple parameterless tool, it covers the basics but lacks essential interpretability details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there is nothing to document. The description does not add parameter meaning because none exist. Baseline 4 is appropriate given no parameters and full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides a 'weighted score over confidence calibration, learning awareness, self-continuity and error monitoring', and distinguishes it as a 'PROXY of meta-representation capacity, not a measurement'. This gives a specific verb-resource relationship and sets it apart from sibling tools like tcai_curiosity or tcai_emotion_appraise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. It does not specify prerequisites, context, or conditions under which it should be invoked. The description merely states what it is, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions 'validate & upload' but does not clarify what happens upon success or failure, whether the operation is destructive, or any side effects. The description is insufficient for a tool with 11 parameters and no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence that packs purpose and scope efficiently. It is front-loaded with the platform name and core action, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, no output schema, no annotations), the description is too minimal. It does not cover return values, side effects, or usage context, leaving significant gaps for an AI agent to understand the tool's full behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (82%), so the schema already documents most parameters. The description adds minimal extra meaning beyond stating 'charge-balanced biphasic stimulation' and 'validate'. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to define, validate, and upload a StimParam for charge-balanced biphasic stimulation. It uses specific verbs (define, validate, upload) and a specific resource (StimParam), making it unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, contexts, or when not to use it. Among siblings like 'np_send_trigger' or 'inject_spikes', no differentiation is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that velocity>0 indicates converging and noveltySpike indicates novel regime. However, it does not clarify if injecting an RPE sample mutates state, nor does it mention any side effects, authorization needs, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise and front-loaded. However, the first sentence packs multiple concepts without clear separation, slightly reducing readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions velocity>0 and noveltySpike as outputs, but does not specify the return format or structure. For a simple read tool with one optional parameter, it is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds 'Optionally inject an RPE sample', which is already covered by the schema's parameter description. No additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool reads meta-learning state (learning velocity, novelty spike) and optionally injects RPE. The verb is implied (read/query), and the resource is the MetaLearningModule port. It distinguishes from sibling tcai_* tools by focusing on learning velocity from RPE-variance dynamics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like tcai_curiosity or tcai_convergence. The description does not mention context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions coupling to multiple subsystems but does not detail effects on organoid state, destructiveness, permissions, or latency. The operational impact is underdescribed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence. It is concise, albeit jargon-heavy, and contains no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool orchestrates a complex closed-loop process, but the description omits return behavior, error conditions, and prerequisites (e.g., organoid activity, ASTRA availability). Without an output schema, more context is essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75% (three of four parameters have descriptions in the schema). The tool description adds no extra parameter context beyond the schema. The baseline is 3 due to high coverage, and no additional value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it reads organoid data and couples it to ASTRA fusion, ROS, and ethics, with optional SNN driving. This distinguishes it from siblings like inject_spikes (simple injection) or np_send_trigger (triggering).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for closed-loop coupling but provides no explicit guidance on when to use it versus alternatives like inject_spikes or simulation_control. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only states the purpose and does not mention whether the tool is read-only, if it modifies state, or any side effects. For a query tool, a read-only hint or clarification that it does not alter the system would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately conveys the tool's purpose. It is front-loaded with the system prefix 'NeuroPlatform v2 DB' and the core function. No unnecessary words or repetitions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three parameters, no output schema, and no annotations, the description is too sparse. It does not explain the default window length, how the spike rate is computed (e.g., scaling from seconds to minutes), or what format the output takes. An agent would benefit from details on parameter effects and return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has three parameters with descriptions for window_sec and fsname, but nonzero_only lacks a description. The tool description does not add any additional meaning beyond the schema; it does not explain the units, defaults, or the role of nonzero_only. With 67% schema description coverage, the description fails to compensate for the missing parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'SpikeCountQuery: spikes/minute per electrode over a time window', providing a specific verb (query), resource (spike count per electrode), and scope (time window). It distinguishes from siblings like np_query_spike_events (events vs counts) and np_count_spikes (name implies similar but description focuses on querying over time).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for querying spike count over a time window but does not explicitly state when to use this tool versus alternatives like np_count_spikes or np_query_spike_events. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It explains the computation but does not disclose side effects, safety, auth requirements, or whether it is read-only. Missing critical behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no wasted words; front-loaded with key purpose, followed by meaning and default. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks description of output (likely a scalar error) despite no output schema. The tool's return format is not stated, leaving the agent uninformed about what it will receive. Incomplete for a computation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds meaning beyond the schema by explaining the embedding's role as a representation vector and its default to GNW broadcast, plus linking to exploration drive. Provides valuable context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes intrinsic-reward/curiosity via prediction error using RNDCuriosity, providing a specific verb-resource pair and distinguishing from siblings like tcai_active_inference by focusing on exploration drive.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives among the many tcai_* tools. The description implies use for novelty or epistemic value but lacks when-not-to-use or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'attention-gated' and 'salience-indexed' processing, but does not disclose side effects, authorization needs, or constraints like memory capacity. Behavioral traits beyond the core action are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with verb and object. No superfluous words, but could be slightly more informative without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of emotional memory and many sibling tools, the description is minimal. No output schema, no return value explanation. Adequate for a storage-only tool but could provide more context about memory integration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 83%, so parameters are mostly documented in the schema. The description adds no extra meaning beyond labeling the mechanism ('attention-gated, salience-indexed'). Baseline 3 is appropriate as schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Store an experience') and specifies the domain ('emotional memory') with qualifiers ('attention-gated, salience-indexed'). It distinguishes from sibling tcai_memory_retrieve, which retrieves memories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use or alternatives. The purpose implies use for storing emotional experiences, but no when-not or prerequisites are mentioned. Sibling names suggest related tools, but the description does not direct agents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the data returned (a snapshot) but does not explicitly state that the operation is read-only, nor does it disclose any side effects, authentication needs, or rate limits. The term 'snapshot' implies non-destructive reading, but this is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise in two sentences, front-loading the key components. However, the reference '(Legros 2026 §3.2)' is cryptic and may not be actionable for the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has zero parameters and no output schema, the description lists the main components of the snapshot, providing sufficient context for an agent to understand what will be returned. It is complete enough for a straightforward retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and the schema coverage is trivially 100%. The baseline for zero parameters is 4, and the description adds no parameter information as none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it is a 'second-order loop snapshot' listing specific components (meta-learning velocity, RND curiosity, etc.), which clarifies its purpose as a composite retrieval tool. It distinguishes from siblings like tcai_meta_learning or tcai_curiosity by being a combined snapshot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like individual tcai_* tools. Lacks any 'when to use' or 'when not to use' information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. However, it fails to mention side effects, destructive nature, system state dependencies, or return behavior. It only gives a high-level action without important behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that immediately states the action and mechanism. It is concise, front-loaded, and contains no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description covers core functionality but omits system state prerequisites (e.g., StimParams must be uploaded), return values, and potential error cases. It is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with clear parameter descriptions. The tool description adds context by explaining that triggers execute 'uploaded StimParams' via a 16-bit trigger array, placing the parameters in the broader system context and adding value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Fire trigger(s)') and the resource ('trigger(s)'), and specifies the mechanism ('execute uploaded StimParams via a 16-bit trigger array'). This distinguishes it from sibling tools like np_configure_stim or np_query_triggers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites such as having uploaded StimParams. It only states its function without any when/when-not or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the retrieval mechanism (cosine similarity, PAD congruence, salience) but does not disclose side effects, authorization requirements, or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, concise sentence that efficiently communicates the tool's purpose without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 optional parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain return format, default behavior when embedding is omitted, or how PAD congruence is computed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 80% (4 of 5 parameters have descriptions). The description adds no additional parameter information beyond what the schema already provides. topK lacks a description in both schema and description, but overall, minimal value added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Retrieve memories' and specifies the retrieval method (blended cosine similarity, PAD congruence, salience). It effectively distinguishes from sibling tools like tcai_memory_store.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for memory retrieval but lacks explicit guidance on when to use this tool versus alternatives, nor does it provide when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the tool returns specific values and that it is 'the principled quantity the halting criterion thresholds on,' implying a read-only operation. However, it does not explicitly state side effects, prerequisites, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence but packs all necessary information upfront. It could be more readable with bullet points, but it is not overly long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description fully enumerates the returned values (F, G(π), task quality, entropy, action) and provides authoritative references, making it complete for an agent to understand what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description does not need to explain parameters. Baseline 4 is appropriate per guidelines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'the REAL variational free energy F, expected free energy G(π), task quality, model entropy, and Dirichlet-learned action.' It references specific academic sources and distinguishes itself from heuristic correlates, providing a precise and unique purpose among sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like tcai_metrics or tcai_convergence. The technical description does not help an agent decide selection context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only report (no parameters, no side effects mentioned), but does not explicitly state it is non-destructive or whether it requires any system state. The description would benefit from stating that it does not modify any data or state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose ('Consciousness proxy report') and lists key contents. It is concise with no wasted words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description sufficiently conveys what the report contains. It does not explain the metrics in detail, but for a simple informational tool this is adequate. It could be improved by noting expected return format or latency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so schema description coverage is 100%. The description lists the metrics included in the report, which adds value beyond the empty input schema. A score of 4 is appropriate as it meets the baseline for no parameters and adds useful context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool produces a 'Consciousness proxy report' and lists specific metrics (GNW metrics, Effective Information, Φ̃-RIIU, composite score). It uses a specific verb and resource, distinguishing it from sibling tools like tcai_metaconsciousness or tcai_self_model which focus on different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or exclusions. Given the abundance of sibling tcai_* tools, the agent would benefit from knowing when to query metrics versus other cognitive functions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool resets multiple system components, implying destructive behavior, but does not specify reversibility, confirmation requirements, or exact effect on each component.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 10 words, efficiently conveying the core functionality without any extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a reset tool with no parameters and no output schema, the description covers the main purpose and components. However, it could be improved by mentioning if the action is irreversible, requires confirmation, or returns a status.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema description coverage is trivially 100%. No parameter info is needed, so the description meets the baseline for tools with no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Reset' and the resource 'TCAI consciousness system', listing the specific components (workspace, memory, emotion, metrics) that are reset. This distinguishes it from sibling tools like snn_reset or export_snapshot.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as snn_reset or tcai_calibrate. No prerequisites or context for invocation are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description discloses the pipeline steps and the early halt behavior. While it doesn't cover side effects or authorization, it provides significant context for a complex tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, information-dense sentence that front-loads the pipeline. It is efficient but could be split for readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 15 parameters, no output schema, and no annotations, the description provides a high-level process but lacks details on return values, interpretation of results, and handling of edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (80%), so many parameters have descriptions. The description adds value by explaining the overall process and the stopWhenSatisfied mechanism, but doesn't detail individual parameters beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it runs ACM cycles and lists the entire pipeline (SNN signals through second-order loop), making the tool's purpose clear. It also distinguishes from sibling tools by focusing on cycle execution.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for running cycles and mentions stopWhenSatisfied for early halting, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it state prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description implies a read-only operation (querying a learned model) but does not explicitly confirm non-destructiveness or disclose any side effects. With no annotations provided, the description carries the full burden, and it falls short of fully disclosing behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys all essential information without extraneous content. Every word is purposeful, making it highly efficient for an AI agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description adequately covers the tool's functionality, including both query and list modes. However, it does not specify the return format or structure of the 'expected-valence map' or 'capability table,' which would be helpful for an agent to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter, but the description adds value by explaining the parameter's purpose ('Action label to query expected valence for') and explicitly mentioning the list mode when the parameter is omitted, which goes beyond the schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'action → expected-valence map (EMA)' and explicitly mentions two usage modes: querying expected outcome for a given action or listing the entire capability table. This distinguishes it from sibling tools which focus on other aspects of the cognitive architecture.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context by stating the two modes: query with an action parameter or list without. However, it does not explicitly compare to alternatives or state when not to use, though the distinct purpose is apparent from the tool name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It transparently describes the process: runs warm-up cycles, records ΔF, sets epsFreeEnergy, and returns values. It does not mention side effects or reversibility, but the core behavioral traits are disclosed. It is sufficiently transparent for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is 4 sentences, front-loading the core purpose. Every sentence adds unique value: purpose, methodology, return values, and motivation. No wasted words, and the structure is logical.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the algorithm, return values, and motivation. It lacks explicit system dependencies or prerequisites, but given the complexity and the fact that no output schema exists, it provides enough context for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions, but the description adds value by explaining the functional role of each parameter (e.g., cycles for warm-up, reward for recording, factor for threshold). This connects the parameters to the algorithm beyond the schema's simple descriptions, justifying a score above the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'Calibrate the halting threshold on the measured ΔF scale instead of a guessed constant', which is a specific verb and resource, clearly stating the tool's unique function. It distinguishes itself from other tcai tools by focusing on calibration, and the reference to a v2.6 critique adds context. The purpose is unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly suggests use when the default threshold is uncalibrated by mentioning 'instead of a guessed constant' and addressing a critique. However, it does not explicitly state prerequisites, when not to use, or list alternatives. The context is clear but lacks explicit decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description comprehensively describes the halting logic: 'loop halts only when variational free energy has settled ... AND realized task quality is high ... AND epistemic value is low, sustained over patience cycles.' It also cites a source (Legros 2026), adding credibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences convey purpose, conditional use, and detailed logic without redundancy. Every sentence adds value; no filler words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool without output schema or annotations, the description provides essential operational context (inspect vs configure, halting condition). It could hint at return format, but the statement 'returns the current satisfaction state and active thresholds' suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 5 parameters have schema descriptions (100% coverage), so baseline is 3. The description adds value by connecting parameters (epsFreeEnergy, minTaskQuality, maxEpistemic, patience) into the overall halting condition, providing context beyond individual definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Inspect or configure the recursive double-loop halting criterion (v2.6)', specifying the verb (inspect/configure) and resource (halting criterion). This distinguishes it from sibling tools like tcai_cycle which focus on execution rather than convergence criteria.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains conditional usage: 'With no arguments, returns ... With arguments, updates them.' This guides when to use inspection vs configuration but does not contrast with alternative tools or specify when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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